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PART I: MAP 2.0 3D Chapter 2 MAP 2.0 3D: A Sequence/Structure Based Server for Protein Engineering 2.1. Abstract The Mutagenesis Assistant Program (MAP) is a web-based tool to provide statistical analyses of the mutational biases of directed evolution experiments on amino acid substitution patterns. MAP analysis assists protein engineering in the benchmarking of random mutagenesis methods that generate single nucleotide mutation in a codon. Herein, we describe a completely renewed and improved version of the MAP server, named as MAP 2.0 3D server that correlates the generated amino acid substitution patterns to the structural information of the target protein. This correlation helps to select more suitable random mutagenesis method with specific biases on amino acid substitution patterns. In particular, the new server represents MAP indicators on secondary and tertiary structure, and correlates them to specific structural components like hydrogen bonds, hydrophobic contacts, salt bridges, solvent accessibility and crystallographic B-factors. Three model proteins (D-amino oxidase, phytase and N-acetylneuraminic acid aldolase) are used to illustrate the novel capability of the server. MAP 2.0 3D server is available publicly at http://map.jacobs-university.de/map3d.html. 38
PART I: MAP 2.0 3D 2.2. Introduction Over the past two decades directed protein evolution has been proven to be a powerful algorithm to tailor protein properties through iterative rounds of random mutagenesis and screening for improved protein variants.[1,2] Directed evolution methods are especially useful for improving properties difficult to rationalize and, hence, to identify amino acids and protein regions that can guide to further enhancements using site directed and saturation mutagenesis methods.[3,4] The success of a directed evolution campaign depends highly on the quality of the mutant library and on the employed random mutagenesis method. Random mutagenesis methods are based on specific error prone polymerase (enzymatic methods), DNA modifying chemicals (e.g. nitrous acid) or mutator strains (e.g. Escherichia coli mutA).[5] The quality of a mutant library is determined by the generated genetic diversity and corresponding protein sequence space.[6] Since the number of muteins boost with the increasing number of amino acid exchanged in the protein, protein engineers are challenged with an astronomically vast sequence space[7]. Despite advances in high-throughput screening, it is very difficult to screen the theoretically generated diversity even in the case of a small protein sequence.[8,9] Therefore, generating high quality mutant libraries enriched with functional trait is of high importance. To deal with the challenge to access and screen such a large sequence space, protein engineers usually adopt two strategic approaches.[10-12] The first approach consists in the random mutagenesis of the target protein and the subsequent identification of ‘mutagenic hot spots’. Random mutagenesis can be followed by recombination of the best variants by site directed mutagenesis or saturation mutagenesis.[13] The second approach involves the identification of a subset of specific residues using rational or semirational design with the help of computational tools.[14] Up to five amino acid positions can be efficiently targeted with focused mutagenesis methods allowing the generation of focused mutant libraries of a number of variants that can be screened with the state of the art in flow cytometry methods.[13] Focused mutagenesis is normally employed to improve the properties of target protein such as activity or selectivity, by mutating residues in close 39
Page 1 and 2: Development and Application of Nove
Page 3 and 4: Funding The work described in this
Page 5 and 6: Abstract In the last decades, enzym
Page 7 and 8: Table of Content Acknowledgement ..
Page 9 and 10: 4.5. References ...................
Page 11 and 12: PART I: CAPDE Chapter 1 Computer-Ai
Page 13 and 14: PART I: CAPDE of protein structure
Page 15 and 16: PART I: CAPDE In this chapter, for
Page 17 and 18: PART I: CAPDE library size and rand
Page 19 and 20: PART I: CAPDE 1.4. Evolutionary con
Page 21 and 22: PART I: CAPDE The web server perfor
Page 23 and 24: PART I: CAPDE based MSA a protein s
Page 25 and 26: PART I: CAPDE name or structure and
Page 27 and 28: PART I: CAPDE for experimental scre
Page 29 and 30: PART I: CAPDE characterizes it bind
Page 31 and 32: PART I: CAPDE stability using inter
Page 33 and 34: PART I: CAPDE in the protein using
Page 35 and 36: PART I: CAPDE Table 1.4: Summarizin
Page 37 and 38: PART I: CAPDE in which the volume o
Page 39 and 40: PART I: CAPDE 21. Chica RA, Doucet
Page 41 and 42: PART I: CAPDE 44. Knoll M, Hamm TM,
Page 43 and 44: PART I: CAPDE 69. Vangone A, Spinel
Page 45 and 46: PART I: CAPDE 95. Emekli U, Schneid
Page 47: PART I: CAPDE 119. Parthiban V, Gro
Page 51 and 52: PART I: MAP 2.0 3D improve the fitn
Page 53 and 54: PART I: MAP 2.0 3D ⎛ 0.0 9.70 19.
Page 55 and 56: PART I: MAP 2.0 3D amino acid (α)
Page 57 and 58: PART I: MAP 2.0 3D of transformatio
Page 59 and 60: PART I: MAP 2.0 3D amino acid oxida
Page 61 and 62: PART I: MAP 2.0 3D aliphatic amino
Page 63 and 64: PART I: MAP 2.0 3D Figure 2.5, char
Page 65 and 66: PART I: MAP 2.0 3D Figure 2.5: Chem
Page 67 and 68: PART I: MAP 2.0 3D 2.4.2. Phytase P
Page 69 and 70: PART I: MAP 2.0 3D Figure 2.6: MAP
Page 71 and 72: PART I: MAP 2.0 3D Figure 2.7: Amin
Page 73 and 74: PART I: MAP 2.0 3D active site resi
Page 75 and 76: PART I: MAP 2.0 3D (δ(282)Q→ch =
Page 77 and 78: PART I: MAP 2.0 3D 11. Turner NJ (2
Page 79 and 80: PART I: MAP 2.0 3D 36. Liao GJ, Lee
Page 81 and 82: PART II: MD Simulation Chapter 3 In
Page 83 and 84: PART II: MD Simulation Force field
Page 85 and 86: PART II: MD Simulation Electrostati
Page 87 and 88: PART II: MD Simulation 3.4. Structu
Page 89 and 90: PART II: MD Simulation where vi and
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PART II: P450BM-3 Reductase Domain
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Curriculum vitae Personal Details N
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Statutory Declaration I, RAJNI VERM
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Development and Application of Novel ... - Jacobs University

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